ML Basics for Transportation & Logistics Applications

This course is designed to provide a comprehensive foundation in the fundamental concepts and techniques of AI learning, specifically tailored for applications in the Transportation & Logistics industries. Participants will gain a deep understanding of the underlying principles and algorithms that power modern AI systems, and learn how to apply these concepts to solve real-world problems in transportation and logistics.

• Understand the fundamental principles and techniques of AI learning and their relevance to the Transportation & Logistics industries

• Apply supervised, unsupervised, and reinforcement learning algorithms to solve transportation and logistics problems

• Develop a strong intuition for model selection, hyperparameter tuning, and performance evaluation

• Implement and deploy AI learning models using industry-standard tools and frameworks

• Communicate the results and insights obtained from AI learning models to both technical and non-technical stakeholders

1. Introduction to AI Learning

• Overview of AI learning and its applications in the Transportation & Logistics industries

• Types of learning: supervised, unsupervised, and reinforcement learning

• The AI learning process: data preparation, model selection, training, evaluation, and deployment

• Hands-on exercises: Setting up the development environment and working with transportation and logistics datasets

2. Supervised Learning

• Overview of supervised learning and its applications in transportation and logistics

• Algorithms for classification and regression (e.g., logistic regression, decision trees, support vector machines)

• Model selection, hyperparameter tuning, and cross-validation techniques

• Hands-on exercises: Implementing supervised learning algorithms for demand forecasting and route optimization

3. Unsupervised Learning

• Overview of unsupervised learning and its applications in transportation and logistics

• Algorithms for clustering, dimensionality reduction, and anomaly detection (e.g., k-means, PCA, autoencoders)

• Techniques for data visualization and interpretation

• Hands-on exercises: Applying unsupervised learning algorithms for customer segmentation and anomaly detection in logistics data

4. Reinforcement Learning

• Overview of reinforcement learning and its applications in transportation and logistics

• Markov Decision Processes (MDPs) and the Bellman equation

• Algorithms for value-based and policy-based reinforcement learning (e.g., Q-learning, SARSA, policy gradients)

• Hands-on exercises: Implementing reinforcement learning algorithms for dynamic route planning and fleet management

5. Advanced Topics and Applications

• Deep learning architectures for AI learning (e.g., convolutional neural networks, recurrent neural networks)

• Transfer learning and domain adaptation techniques for transportation and logistics data

• Real-world case studies and applications of AI learning in the Transportation & Logistics industries

• Hands-on exercises: Developing an end-to-end AI learning pipeline for a transportation or logistics problem

• Strong understanding of mathematics, including linear algebra, calculus, and probability theory

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and algorithms